It's an anecdote which is meant to illustrate a widely observed behavior among many humans: we're better at accounting for frequent errors than we are at accoun

It's an anecdote which is meant to illustrate a widely observed behavior among many humans: we're better at accounting for frequent errors than we are at accounting for rare ones. This is well established in the field of human reliability analysis (HRA). Take a look at the HRA method HEART (https://www.epd.gov.hk/eia/register/report/eiareport/eia_2242014/EIA/app/app12.10.pdf https://www.epd.gov.hk/eia/register/report/eiareport/eia_224...). There are three different error producing conditions (EPCs) related to rare failures (EPCs 1, 3, and 12 in table 1.3). As for my argument that people who frequently work with this technology aren't going to be so susceptible to "shiny-confident-robot" - that is based on my first hand observation. But the counterargument (people trust LLMs too much) is not based in data either. The Pearce, et. al paper everyone cites about security vulnerabilities didn't look at how much people trust the code, just how frequently it gives wrong answers (https://arxiv.org/pdf/2108.09293.pdf https://arxiv.org/pdf/2108.09293.pdf). An actual user study (n=20) showed that Copilot users engage critically with the tool by examining and testing suggestions, comparing multiple suggestions, and crosschecking it against documentation (https://arxiv.org/pdf/2206.15000.pdf https://arxiv.org/pdf/2206.15000.pdf).